“Baby Mental Life: Study 2” was conducted on MTurk on 2018-08-04.
Our planned sample was 300 participants, and we anticipated that roughly 80% of recruited participants would pass all of our attention checks, so we initially recruited 378 participants (on the idea that ~80% of 378 ~ 300 participants; note that for administrative purposes we need to recuit participants in batches that were divisible by 9). After filtering out participants who failed at least one of our attention checks, we ended up retaining fewer than 300 participants, so we recruited an additional 16 participants for a total of 394 people recruited. At each stage, we recruited women and men through separate studies, in hopes of acquiring a roughly equal split between genders.
In the end, we ended up with a sample of 304 participants who passed our attention checks, 237 of whom came from unique GPS coordinates.
For this first pass, these data INCLUDE participants where there is another participant with an identical set of GPS coordinates as recorded by Qualtrics. Excluding these participants would exclude 67 participants.
Each participant assessed children’s mental capacities at 13 target ages between the ages of 0 and 5 years. For each target, they rated 20 mental capacities on a scale from 0 (not at all capable) to 100 (completely capable).
For more details about the study, see our preregistration here.
NAs introduced by coercionattributes are not identical across measure variables;
they will be droppedJoining, by = "question_qualtrics"
Study 1 EFA
Joining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"

EFA
To test H1, we planned to conduct an exploratory factor analysis (EFA) collapsing across all 13 target characters (and treating an individual participant’s responses to each character as if they were independent data points) - see the preregistration for more details.
As with Study 1, we planned to examine three factor retention protocols in order to determine how many factors to retain: Parallel analysis, minimizing BIC, and a set of preset criteria outlined in Weisman et al. (2017). Here we look at each solution in turn.
We predicted that we’d see a similar factor structure to that found in Study 1.
Rotation choices
We planned to examine oblimin-rotated solutions (which allow factors to correlate), but you could examine other rotation options by selecting a different rotation type here.
Parallel analysis
How many factors to retain?
Parallel analysis suggests that the number of factors = 4 and the number of components = 4
Call: fa.parallel(x = d_all, plot = F)
Parallel analysis suggests that the number of factors = 4 and the number of components = 4
Eigen Values of
What are these factors?
Joining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"

These factors look extremely similar to what we saw in Study 1 (see above). I (Kara) would say that H1 is strongly supported.
Which capacities are attributed to which targets?
We could look at factor scores using the Study 2 EFA to see which capacities were attributed to which targets. This is not the primary way we planned to investigate this - this was listed as a “follow-up analysis” - but I’m putting it here so that it’s in close proximity to the EFA results for ease of interpretation.
Age as numeric (raw)
Ignoring unknown aesthetics: y

Age as ordinal
Ignoring unknown aesthetics: y

And here’s a close look at all of the raw data (color-coded according to the Study 2 EFA results):
Joining, by = "capacity"
|============================= | 59% ~1 s remaining
|============================== | 60% ~1 s remaining
|=============================== | 62% ~1 s remaining
|================================ | 64% ~1 s remaining
|================================= | 66% ~1 s remaining
|================================== | 68% ~1 s remaining
|================================== | 70% ~1 s remaining
|=================================== | 72% ~1 s remaining
|==================================== | 73% ~1 s remaining
|===================================== | 75% ~1 s remaining
|====================================== | 77% ~1 s remaining
|======================================= | 79% ~1 s remaining
|======================================== | 81% ~1 s remaining
|========================================= | 83% ~1 s remaining
|========================================= | 83% ~1 s remaining
|========================================== | 85% ~1 s remaining
|=========================================== | 87% ~0 s remaining
|============================================ | 89% ~0 s remaining
|============================================= | 91% ~0 s remaining
|============================================== | 93% ~0 s remaining
|=============================================== | 95% ~0 s remaining
|=============================================== | 96% ~0 s remaining
|================================================ | 98% ~0 s remaining
|================================================= |100% ~0 s remaining
Joining, by = c("capacity", "factor", "order")

Joining, by = "capacity"

Minimizing BIC
How many factors to retain?
Very Simple Structure
Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
VSS complexity 1 achieves a maximimum of 0.85 with 1 factors
VSS complexity 2 achieves a maximimum of 0.96 with 2 factors
The Velicer MAP achieves a minimum of 0.02 with 4 factors
BIC achieves a minimum of -153.95 with 8 factors
Sample Size adjusted BIC achieves a minimum of 30.35 with 8 factors
Statistics by number of factors
What are these factors?
convergence not obtained in GPFoblq. 1000 iterations used.
Joining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"

A more complex picture, but the first 4 factors look similar to what we get through parallel analysis. (I think something similar happened with Study 1, but we should go back and compare.)
Which capacities are attributed to which targets?
Ignoring unknown aesthetics: y

We’ll skip regression analyses and other plots for now.
Preset retention criteria
How many factors to retain?
[1] "Preset criteria suggest retaining 4 factors"
This gives the same solution as parallel analysis - 4 factors :)
Projecting into Study 1 factor space
I (Kara) made a big mistake in thinking through this: I thought we could project a 20-variable dataset into a 60-variable dataset using the predict.psych() function, but we can’t!
I’ve tried to hack together a way to do this, by replacing all missing values at random (either within the full range of the scale, or around the midpoint, or near 0) - but I haven’t gotten anything to work. As you can see below (replacing missing values at random between 0-5), you see slight increases across all factors, and the most dramatic increase for Factor 4 - but I think this is because that factor is least well-defined in the Study 1 EFA solution? I think we need to focus on analyzing factor scores from our Study 2 EFA. I’m sorry for this mistake!

Regression models in Study 2 factor space
Here’s a multilevel linear regression on these factor scores, with random intercepts and slopes (for target and factor) by participant. Target is coded as numeric, with only the linear contrast.
If we try to run the model above (our planned analysis), we get an error: “Model is nearly unidentifiable: very large eigenvalue.” The error suggests rescaling variables, which solves the problem. Here I’ve re-scaled by divided age in months by 12, to get age in years. Let’s make sure to talk about this.
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: score ~ target_num * factor + (target_num + factor | ResponseId)
Data: efa_all_par_scores %>% mutate(target_num = target_num/12)
REML criterion at convergence: 26763.9
Scaled residuals:
Min 1Q Median 3Q Max
-7.6052 -0.4546 0.0520 0.5209 4.4725
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 0.37561 0.6129
target_num 0.01555 0.1247 -0.62
factor1 0.18356 0.4284 0.48 -0.35
factor2 0.28893 0.5375 -0.53 0.22 -0.43
factor3 0.30770 0.5547 0.21 0.26 -0.11 -0.69
Residual 0.24497 0.4949
Number of obs: 15808, groups: ResponseId, 304
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.699e-01 3.555e-02 3.030e+02 -10.407 < 2e-16 ***
target_num 2.622e-01 7.575e-03 3.030e+02 34.608 < 2e-16 ***
factor1 8.014e-02 2.622e-02 3.387e+02 3.056 0.00242 **
factor2 -2.776e-01 3.216e-02 3.260e+02 -8.633 2.69e-16 ***
factor3 2.634e-01 3.310e-02 3.247e+02 7.957 2.96e-14 ***
target_num:factor1 -5.679e-02 4.324e-03 1.429e+04 -13.133 < 2e-16 ***
target_num:factor2 1.967e-01 4.324e-03 1.429e+04 45.498 < 2e-16 ***
target_num:factor3 -1.867e-01 4.324e-03 1.429e+04 -43.171 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
As we predicted (H2), we see dramatic increases in mental capacity attributions across the age range (main effect of target_num).
And also as we predicted (H1), we see differences across factors in where newborns are perceived to start off: Relative to the grand mean, newborns are perceived to start off with more “negative emotions” (distress, frustration, etc.; main effect of factor1), less/fewer capacities in the domain of “cognition and control” (emotional control, self control, etc.; main effect of factor2), and relatively more “bodily sensations” (pain, fatigue, etc.; main effect of factor3). (We could recode this to look at factor4, or just eyeball it from the plot.) Also as predicted, we see that the perceived changes across age vary dramatically across factors: “negative emotions” are perceived to change relatively less over development, “cognition and control” are perceived to change much more over development, and “bodily sensations” are predicted to chagne relatively less.
This is all very much in line with our preregistered hypotheses :)
Now let’s see what the polynomial effects look like (again, looking at age in years instead of months). As we expected, including all of the polynomial effects as random slopes caused the model not to converge (I think we must be calculating df wrong), so I implemented our preregistered remedy and included only the linear effect as a random slope.
convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceededModel failed to converge with max|grad| = 0.237067 (tol = 0.002, component 1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: score ~ poly(target_num, 3) * factor + (poly(target_num, 1) +
factor | ResponseId)
Data: efa_all_par_scores %>% mutate(target_num = target_num/12)
REML criterion at convergence: 23281.6
Scaled residuals:
Min 1Q Median 3Q Max
-8.4079 -0.4921 0.0054 0.5502 4.9596
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 0.2744 0.5238
poly(target_num, 1) 615.9142 24.8176 -0.39
factor1 0.1866 0.4320 0.44 -0.34
factor2 0.2918 0.5402 -0.54 0.22 -0.43
factor3 0.3089 0.5558 0.33 0.25 -0.11 -0.69
Residual 0.1928 0.4391
Number of obs: 15808, groups: ResponseId, 304
Fixed effects:
Estimate Std. Error df t value
(Intercept) 5.963e-14 3.024e-02 3.024e+02 0.000
poly(target_num, 3)1 5.197e+01 1.490e+00 3.084e+02 34.891
poly(target_num, 3)2 -2.111e+01 4.391e-01 1.427e+04 -48.078
poly(target_num, 3)3 9.471e+00 4.391e-01 1.427e+04 21.568
factor1 4.472e-14 2.550e-02 3.028e+02 0.000
factor2 -3.966e-14 3.157e-02 3.030e+02 0.000
factor3 -3.801e-14 3.245e-02 3.058e+02 0.000
poly(target_num, 3)1:factor1 -1.126e+01 7.606e-01 1.427e+04 -14.803
poly(target_num, 3)2:factor1 3.958e+00 7.606e-01 1.427e+04 5.204
poly(target_num, 3)3:factor1 -2.147e+00 7.606e-01 1.427e+04 -2.823
poly(target_num, 3)1:factor2 3.900e+01 7.606e-01 1.427e+04 51.281
poly(target_num, 3)2:factor2 2.110e+00 7.606e-01 1.427e+04 2.774
poly(target_num, 3)3:factor2 -6.915e+00 7.606e-01 1.427e+04 -9.092
poly(target_num, 3)1:factor3 -3.701e+01 7.606e-01 1.427e+04 -48.658
poly(target_num, 3)2:factor3 1.306e+01 7.606e-01 1.427e+04 17.167
poly(target_num, 3)3:factor3 -5.433e+00 7.606e-01 1.427e+04 -7.143
Pr(>|t|)
(Intercept) 1.00000
poly(target_num, 3)1 < 2e-16 ***
poly(target_num, 3)2 < 2e-16 ***
poly(target_num, 3)3 < 2e-16 ***
factor1 1.00000
factor2 1.00000
factor3 1.00000
poly(target_num, 3)1:factor1 < 2e-16 ***
poly(target_num, 3)2:factor1 1.98e-07 ***
poly(target_num, 3)3:factor1 0.00477 **
poly(target_num, 3)1:factor2 < 2e-16 ***
poly(target_num, 3)2:factor2 0.00555 **
poly(target_num, 3)3:factor2 < 2e-16 ***
poly(target_num, 3)1:factor3 < 2e-16 ***
poly(target_num, 3)2:factor3 < 2e-16 ***
poly(target_num, 3)3:factor3 9.59e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
convergence code: 1
Model failed to converge with max|grad| = 0.237067 (tol = 0.002, component 1)
Lots to sift through here, but in general we see that the effect of target age on mental capacity attributions definitely has linear, quadratic, and cubic components, all three of which seem to vary substantially across factors. Pretty much all of these differences are “significant” (if you consider |t| > 2 to be “significant”) - for interpretation, I would need to look closer at the plot. Let’s pull it up again here, with blue lines approximating the formula y ~ poly(x, 3):
Ignoring unknown aesthetics: y

We can talk through these interpretations together - but I find the difference between Factor 2 (“cognition & control”) and Factor 4 (“positive emotions”) to be especially interesting!
Planning for S3 prereg
Joining, by = "capacity"
Column `capacity` joining character vector and factor, coercing into character vectorJoining, by = "capacity"
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
response ~ target_num * domain + (target_num + domain | ResponseId) +
(target_num | capacity)
Data: d_temp_culled %>% mutate(target_num = target_num/12)
REML criterion at convergence: 276364.8
Scaled residuals:
Min 1Q Median 3Q Max
-5.5999 -0.4567 0.0285 0.5102 5.4324
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 241.394 15.537
target_num 12.017 3.467 -0.58
domain1 115.871 10.764 -0.48 0.49
domain2 199.249 14.116 -0.28 0.15 -0.44
domain3 202.944 14.246 0.58 -0.32 -0.21 -0.57
capacity (Intercept) 98.634 9.931
target_num 4.021 2.005 -0.98
Residual 320.297 17.897
Number of obs: 31616, groups: ResponseId, 304; capacity, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 57.758 3.625 4.530 15.933 3.76e-05 ***
target_num 7.579 0.739 4.647 10.256 0.000229 ***
domain1 33.744 6.117 4.082 5.516 0.004968 **
domain2 -45.589 6.140 4.142 -7.425 0.001529 **
domain3 7.296 6.141 4.145 1.188 0.298402
target_num:domain1 -6.308 1.233 3.999 -5.116 0.006907 **
target_num:domain2 6.709 1.233 3.999 5.442 0.005541 **
target_num:domain3 -1.445 1.233 3.999 -1.172 0.306238
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
response ~ target_num * domain + (target_num + domain | ResponseId) +
(1 | capacity)
Data: d_temp_culled %>% mutate(target_num = target_num/12)
REML criterion at convergence: 276844.7
Scaled residuals:
Min 1Q Median 3Q Max
-5.5281 -0.4508 0.0443 0.5054 5.4043
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 241.30 15.534
target_num 12.00 3.464 -0.58
domain1 115.72 10.757 -0.48 0.49
domain2 199.10 14.110 -0.28 0.15 -0.44
domain3 202.79 14.241 0.58 -0.33 -0.21 -0.57
capacity (Intercept) 51.56 7.181
Residual 325.55 18.043
Number of obs: 31616, groups: ResponseId, 304; capacity, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 57.7579 2.6940 5.0546 21.440 3.7e-06 ***
target_num 7.5794 0.2088 303.0006 36.297 < 2e-16 ***
domain1 33.7440 4.4466 4.1692 7.589 0.001368 **
domain2 -45.5894 4.4773 4.2856 -10.182 0.000367 ***
domain3 7.2957 4.4786 4.2908 1.629 0.173807
target_num:domain1 -6.3077 0.1115 30088.9984 -56.590 < 2e-16 ***
target_num:domain2 6.7086 0.1115 30088.9984 60.186 < 2e-16 ***
target_num:domain3 -1.4450 0.1115 30088.9985 -12.963 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: score ~ target_num * domain + (target_num | ResponseId)
Data:
d_temp_culled %>% mutate(target_num = target_num/12) %>% group_by(domain,
target_num, ResponseId) %>% summarise(score = mean(response,
na.rm = T)) %>% ungroup() %>% distinct()
REML criterion at convergence: 138809.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.7636 -0.5695 0.0200 0.6602 4.9951
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 234.91 15.327
target_num 10.57 3.251 -0.58
Residual 347.42 18.639
Number of obs: 15808, groups: ResponseId, 304
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 57.7579 0.9013 303.0000 64.084 <2e-16 ***
target_num 7.5794 0.2088 302.9998 36.297 <2e-16 ***
domain1 33.7440 0.3446 15194.0000 97.928 <2e-16 ***
domain2 -45.5894 0.3446 15194.0000 -132.304 <2e-16 ***
domain3 7.2957 0.3446 15194.0000 21.173 <2e-16 ***
target_num:domain1 -6.3077 0.1628 15194.0000 -38.735 <2e-16 ***
target_num:domain2 6.7086 0.1628 15194.0000 41.197 <2e-16 ***
target_num:domain3 -1.4450 0.1628 15194.0000 -8.873 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: response ~ target_num + (1 | ResponseId) + (1 + target_num |
capacity)
Data:
d_temp_culled %>% mutate(target_num = target_num/12) %>% filter(domain ==
"NEG")
REML criterion at convergence: 71637.9
Scaled residuals:
Min 1Q Median 3Q Max
-4.3024 -0.5181 0.0924 0.6438 4.6977
Random effects:
Groups Name Variance Std.Dev. Corr
ResponseId (Intercept) 588.487 24.259
capacity (Intercept) 102.537 10.126
target_num 5.404 2.325 -1.00
Residual 440.359 20.985
Number of obs: 7904, groups: ResponseId, 304; capacity, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 65.054 7.301 1.077 8.910 0.0611 .
target_num 6.134 1.651 1.000 3.716 0.1673
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: response ~ target_num + (1 | ResponseId) + (1 | capacity)
Data:
d_temp_culled %>% mutate(target_num = target_num/12) %>% filter(domain ==
"NEG")
REML criterion at convergence: 71757.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.1585 -0.5067 0.1344 0.6182 4.5498
Random effects:
Groups Name Variance Std.Dev.
ResponseId (Intercept) 588.22 24.253
capacity (Intercept) 46.84 6.844
Residual 447.36 21.151
Number of obs: 7904, groups: ResponseId, 304; capacity, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 65.0535 5.0455 1.1758 12.89 0.0328 *
target_num 6.1344 0.1509 7597.9999 40.66 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
---
title: "Baby Mental Life: Study 2"
subtitle: "Preregistered analyses"
date: 2019-05-15
output: 
  html_notebook:
    toc: true
    toc_float: true
---

```{r global_options, include = F}
knitr::opts_chunk$set(fig.width = 3, fig.asp = 0.67, include = T, echo = F)
```

"Baby Mental Life: Study 2" was conducted on MTurk on 2018-08-04.

Our planned sample was 300 participants, and we anticipated that roughly 80% of recruited participants would pass all of our attention checks, so we initially recruited 378 participants (on the idea that ~80% of 378 ~ 300 participants; note that for administrative purposes we need to recuit participants in batches that were divisible by 9). After filtering out participants who failed at least one of our attention checks, we ended up retaining fewer than 300 participants, so we recruited an additional 16 participants for a total of 394 people recruited. At each stage, we recruited women and men through separate studies, in hopes of acquiring a roughly equal split between genders.

In the end, we ended up with a sample of 304 participants who passed our attention checks, 237 of whom came from unique GPS coordinates.

**For this first pass, these data _INCLUDE_ participants where there is another participant with an identical set of GPS coordinates as recorded by Qualtrics. Excluding these participants would exclude 67 participants.**

Each participant assessed children's mental capacities at 13 target ages between the ages of 0 and 5 years. For each target, they rated 20 mental capacities on a scale from 0 (not at all capable) to 100 (completely capable). 

For more details about the study, see our preregistration [here](https://osf.io/j72dg/). 

```{r}
# load required libraries
library(tidyverse)
library(langcog) # source: https://github.com/langcog/langcog-package
library(psych)
library(lme4)

# set theme for ggplots
theme_set(theme_bw())

chosen_rot <- "oblimin"
```

```{r}
# run source code (extra home-made functions)
source("./scripts/max_factors_efa.R")
source("./scripts/plot_fun.R")
source("./scripts/reten_fun.R")
source("./scripts/data_prep.R")
```


# Study 1 EFA

```{r}
# load in EFA results from study 1
efa_S1 <- readRDS("../study 1/s1_efa.rds")
```

```{r, fig.width = 4, fig.asp = 1.5}
heatmap_fun(efa_S1) + 
  labs(title = paste0("STUDY 1 Parallel Analysis (rotation: ", chosen_rot, ")"),
       subtitle = "'% var.' indicates the amount of shared variance explained (total = 100%)")
```

# EFA

To test H1, we planned to conduct an exploratory factor analysis (EFA) collapsing across all 13 target characters (and treating an individual participant's responses to each character as if they were independent data points) - see the preregistration for more details.  

As with Study 1, we planned to examine three factor retention protocols in order to determine how many factors to retain: Parallel analysis, minimizing BIC, and a set of preset criteria outlined in Weisman et al. (2017). Here we look at each solution in turn.

We predicted that we'd see a similar factor structure to that found in Study 1.


## Rotation choices

We planned to examine oblimin-rotated solutions (which allow factors to correlate), but you could examine other rotation options by selecting a different rotation type here.

```{r}
chosen_rot <- "oblimin" # preregistered: factors allowed to correlate
# chosen_rot <- "varimax" # orthogonal: factors forced not to correlate
# chosen_rot <- "none" # no rotation
```


## Parallel analysis

### How many factors to retain?

```{r}
reten_all_PA <- fa.parallel(d_all, plot = F); reten_all_PA
reten_all_par <- reten_all_PA$nfact
```

### What are these factors?

```{r}
efa_all_par <- fa(d_all, nfactors = reten_all_par, rotate = chosen_rot,
                  scores = "tenBerge", impute = "median")
```

```{r, fig.width = 4, fig.asp = 0.7}
heatmap_fun(efa_all_par) + 
  labs(title = paste0("Parallel Analysis (rotation: ", chosen_rot, ")"),
       subtitle = "'% var.' indicates the amount of shared variance explained (total = 100%)")
```

These factors look extremely similar to what we saw in Study 1 (see above). I (Kara) would say that H1 is strongly supported.


### Which capacities are attributed to which targets?

We could look at factor scores using the Study 2 EFA to see which capacities were attributed to which targets. This is not the primary way we planned to investigate this - this was listed as a "follow-up analysis" - but I'm putting it here so that it's in close proximity to the EFA results for ease of interpretation.

#### Age as numeric (raw)

```{r, fig.width = 4, fig.asp = 0.5}
scoresplot_fun(efa_all_par, target = "all (study 2)", 
               target_encoding = "numeric") +
  scale_x_continuous(breaks = seq(0, 60, 12)) +
  labs(title = "Parallel Analysis") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

#### Age as numeric (square-root-transformed)

```{r, fig.width = 4, fig.asp = 0.5}
scoresplot_fun(efa_all_par, target = "all (study 2)", 
               target_encoding = "numeric") +
  scale_x_continuous(breaks = seq(0, 60, 12), trans = "sqrt") +
  labs(title = "Parallel Analysis", 
       x = "age after square-root transformation (months)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```


#### Age as ordinal

```{r, fig.width = 4, fig.asp = 0.5}
scoresplot_fun(efa_all_par, target = "all (study 2)", 
               target_encoding = "ordinal") +
  labs(title = "Parallel Analysis", 
       x = "age (ordinal)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```


And here's a close look at all of the raw data (color-coded according to the Study 2 EFA results):

```{r, fig.width = 8, fig.asp = 0.4}
itemsplot_fun(efa_all_par, target = "all (study 2)") + 
  labs(title = "Parallel Analysis")
```

```{r, fig.width = 6, fig.asp = 0.7}
d_all %>%
  rownames_to_column("subid_target") %>%
  mutate(subid = gsub("_.*$", "", subid_target),
         target = gsub("^.*_", "", subid_target),
         target_num = recode(target,
                             "target00mo" = 0,
                             "target0Xmo" = 4/30,
                             "target01mo" = 1,
                             "target02mo" = 2,
                             "target04mo" = 4,
                             "target06mo" = 6,
                             "target09mo" = 9,
                             "target12mo" = 12,
                             "target18mo" = 18,
                             "target24mo" = 24,
                             "target36mo" = 36,
                             "target48mo" = 48,
                             "target60mo" = 60),
         target_ord = recode_factor(target,
                                    "target00mo" = "newborns",
                                    "target0Xmo" = "4-day-olds",
                                    "target01mo" = "1-month-olds",
                                    "target02mo" = "2-month-olds",
                                    "target04mo" = "4-month-olds",
                                    "target06mo" = "6-month-olds",
                                    "target09mo" = "9-month-olds",
                                    "target12mo" = "12-month-olds",
                                    "target18mo" = "18-month-olds",
                                    "target24mo" = "2-year-olds",
                                    "target36mo" = "3-year-olds",
                                    "target48mo" = "4-year-olds",
                                    "target60mo" = "5-year-olds")) %>%
  select(-c(subid_target, target)) %>%
  gather(capacity, response, -c(subid, starts_with("target"))) %>%
  full_join(efa_all_par$loadings[] %>%
              data.frame() %>%
              rownames_to_column("capacity") %>%
              gather(factor, loading, -capacity) %>%
              group_by(capacity) %>%
              top_n(1, abs(loading)) %>%
              ungroup() %>%
              arrange(factor, desc(abs(loading))) %>%
              mutate(order = 1:20) %>%
              select(capacity, factor, order)) %>%
  # ggplot(aes(x = target_ord, y = response, color = factor)) +
  ggplot(aes(x = target_num, y = response, color = factor)) +
  facet_wrap(~ reorder(capacity, order)) +
  geom_line(aes(group = subid), alpha = 0.1) +
  geom_smooth(aes(group = capacity),
              method = "lm", formula = "y ~ poly(x, 3)",
              color = "black") +
  scale_color_brewer(palette = "Set2", guide = "none") +
  # scale_x_discrete("target age (ordinal)") +
  scale_x_continuous("target age (months)", breaks = seq(0, 60, 12)) +
  # scale_x_continuous("age after square-root transformation (months)", 
  #                    breaks = seq(0, 60, 12), trans = "sqrt") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  guides(color = guide_legend(override.aes = list(alpha = 1)))
```


## Minimizing BIC

### How many factors to retain?

```{r}
reten_all_vss <- VSS(d_all, plot = F); reten_all_vss
reten_all_bic <- data.frame(reten_all_vss$vss.stats %>%
  rownames_to_column("nfactors") %>%
  top_n(-1, BIC) %>%
  select(nfactors))$nfactors %>% as.numeric()
```

### What are these factors?

```{r}
efa_all_bic <- fa(d_all, nfactors = reten_all_bic, rotate = chosen_rot,
                  scores = "tenBerge", impute = "median")
```

```{r, fig.width = 4, fig.asp = 0.7}
heatmap_fun(efa_all_bic) + 
  labs(title = paste0("Minimizing BIC (rotation: ", chosen_rot, ")"),
       subtitle = "'% var.' indicates the amount of shared variance explained (total = 100%)")
```

A more complex picture, but the first 4 factors look similar to what we get through parallel analysis. (I think something similar happened with Study 1, but we should go back and compare.)

### Which capacities are attributed to which targets?

```{r, fig.width = 4, fig.asp = 0.7}
scoresplot_fun(efa_all_bic, target = "all (study 2)") + 
  labs(title = "Minimizing BIC") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

We'll skip regression analyses and other plots for now.


## Preset retention criteria

### How many factors to retain?

```{r}
reten_all_k <- reten_fun(d_all, rot_type = chosen_rot)
print(paste("Preset criteria suggest retaining", reten_all_k, "factors"))
```

This gives the same solution as parallel analysis - 4 factors :)


# Projecting into Study 1 factor space

**I (Kara) made a big mistake in thinking through this: I thought we could project a 20-variable dataset into a 60-variable dataset using the `predict.psych()` function, but we can't!**

I've tried to hack together a way to do this, by replacing all missing values at random (either within the full range of the scale, or around the midpoint, or near 0) - but I haven't gotten anything to work. As you can see below (replacing missing values at random between 0-5), you see slight increases across all factors, and the most dramatic increase for Factor 4 - but I think this is because that factor is least well-defined in the Study 1 EFA solution?  I think we need to focus on analyzing factor scores from our Study 2 EFA. I'm sorry for this mistake!

```{r}
extra_var <- rownames(efa_S1$loadings)[!rownames(efa_S1$loadings) %in% rownames(efa_all_par$loadings)]

temp <- d_all %>%
  rownames_to_column("subid") %>%
  mutate(being_afraid_of_somebody = round(runif(3952, 0, 5)), 
         being_angry_at_somebody = round(runif(3952, 0, 5)),
         being_aware_of_things = round(runif(3952, 0, 5)), 
         being_comforted_by_physical_touch = round(runif(3952, 0, 5)),
         calming_themselves_down = round(runif(3952, 0, 5)), 
         detecting_danger = round(runif(3952, 0, 5)), 
         feeling_annoyed = round(runif(3952, 0, 5)), 
         feeling_bored = round(runif(3952, 0, 5)), 
         feeling_calm = round(runif(3952, 0, 5)),
         feeling_confused = round(runif(3952, 0, 5)), 
         feeling_embarrassed = round(runif(3952, 0, 5)), 
         feeling_guilty = round(runif(3952, 0, 5)),
         feeling_hopeless = round(runif(3952, 0, 5)), 
         feeling_loved = round(runif(3952, 0, 5)), 
         feeling_neglected = round(runif(3952, 0, 5)),
         feeling_pleasure = round(runif(3952, 0, 5)), 
         feeling_pride = round(runif(3952, 0, 5)), 
         feeling_sad = round(runif(3952, 0, 5)),
         feeling_safe = round(runif(3952, 0, 5)), 
         feeling_scared = round(runif(3952, 0, 5)), 
         feeling_textures = round(runif(3952, 0, 5)),
         feeling_thirsty = round(runif(3952, 0, 5)), 
         feeling_too_hot_or_too_cold = round(runif(3952, 0, 5)),
         feeling_worried = round(runif(3952, 0, 5)), 
         focusing_on_a_goal = round(runif(3952, 0, 5)), 
         getting_angry = round(runif(3952, 0, 5)),
         getting_hurt_feelings = round(runif(3952, 0, 5)), 
         getting_pleasure_from_music = round(runif(3952, 0, 5)),
         having_goals = round(runif(3952, 0, 5)), 
         having_thoughts = round(runif(3952, 0, 5)), 
         having_wants_and_desires = round(runif(3952, 0, 5)),
         imagining_things = round(runif(3952, 0, 5)), 
         listening_to_somebody = round(runif(3952, 0, 5)), 
         making_choices = round(runif(3952, 0, 5)),
         recognizing_others_emotions = round(runif(3952, 0, 5)), 
         recognizing_somebody_else = round(runif(3952, 0, 5)),
         remembering_things = round(runif(3952, 0, 5)), 
         seeing = round(runif(3952, 0, 5)), 
         thinking_before_they_act = round(runif(3952, 0, 5)),
         understanding_what_somebody_else_is_thinking = round(runif(3952, 0, 5))) %>%
  column_to_rownames("subid")

scores_project <- predict.psych(object = efa_S1, data = temp)
```

```{r, fig.width = 4, fig.asp = 0.7}
scores_project %>%
  data.frame() %>%
  rownames_to_column("subid_target") %>%
  mutate(subid = gsub("_.*$", "", subid_target),
         target = gsub("^.*_", "", subid_target),
         target_num = recode(target,
                             "target00mo" = 0,
                             "target0Xmo" = round(4/30, 3),
                             "target01mo" = 1,
                             "target02mo" = 2,
                             "target04mo" = 4,
                             "target06mo" = 6,
                             "target09mo" = 9,
                             "target12mo" = 12,
                             "target18mo" = 18,
                             "target24mo" = 24,
                             "target36mo" = 36,
                             "target48mo" = 48,
                             "target60mo" = 60),
         target_ord = recode_factor(target,
                                    "target00mo" = "newborns",
                                    "target0Xmo" = "4-day-olds",
                                    "target01mo" = "1-month-olds",
                                    "target02mo" = "2-month-olds",
                                    "target04mo" = "4-month-olds",
                                    "target06mo" = "6-month-olds",
                                    "target09mo" = "9-month-olds",
                                    "target12mo" = "12-month-olds",
                                    "target18mo" = "18-month-olds",
                                    "target24mo" = "2-year-olds",
                                    "target36mo" = "3-year-olds",
                                    "target48mo" = "4-year-olds",
                                    "target60mo" = "5-year-olds")) %>%
  select(-subid_target) %>%
  gather(factor, score, -c(subid, starts_with("target"))) %>%
  ggplot(aes(x = target_num, y = score, color = factor)) +
  facet_grid(~ factor) +
  geom_line(aes(group = subid), alpha = 0.1) +
  scale_x_continuous(breaks = seq(0, 60, 12)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "none") +
  labs(title = "Kara's attempt to project into the Study 1 space",
       subtitle = "Replaced all missing values (40 per participant) with a random integer between 0-5",
       x = "target age (months)", y = "factor score")
```



# Regression models in Study 2 factor space

Here's a multilevel linear regression on these factor scores, with random intercepts and slopes (for target and factor) by participant. Target is coded as numeric, with only the linear contrast.

```{r}
efa_all_par_scores <- efa_all_par$scores[] %>%
  data.frame() %>%
  rownames_to_column("subid") %>%
  mutate(target = gsub("^.*_target", "target", subid),
         ResponseId = gsub("_target.*$", "", subid),
         target_num = recode(target,
                             "target00mo" = 0,
                             "target0Xmo" = round(4/30, 3),
                             "target01mo" = 1,
                             "target02mo" = 2,
                             "target04mo" = 4,
                             "target06mo" = 6,
                             "target09mo" = 9,
                             "target12mo" = 12,
                             "target18mo" = 18,
                             "target24mo" = 24,
                             "target36mo" = 36,
                             "target48mo" = 48,
                             "target60mo" = 60),
         target_ord = recode_factor(target,
                                    "target00mo" = "newborns",
                                    "target0Xmo" = "4-day-olds",
                                    "target01mo" = "1-month-olds",
                                    "target02mo" = "2-month-olds",
                                    "target04mo" = "4-month-olds",
                                    "target06mo" = "6-month-olds",
                                    "target09mo" = "9-month-olds",
                                    "target12mo" = "12-month-olds",
                                    "target18mo" = "18-month-olds",
                                    "target24mo" = "2-year-olds",
                                    "target36mo" = "3-year-olds",
                                    "target48mo" = "4-year-olds",
                                    "target60mo" = "5-year-olds")) %>%
  select(-subid, -target) %>%
  gather(factor, score, -starts_with("target"), -ResponseId) %>%
  mutate_at(vars(factor), funs(factor))

contrasts(efa_all_par_scores$factor) <- contr.sum(reten_all_par)

# r_all_par <- lmer(score ~ target_num * factor
#                   + (target_num + factor | ResponseId),
#                   efa_all_par_scores)
# summary(r_all_par, corr = F)
```

If we try to run the model above (our planned analysis), we get an error: "Model is nearly unidentifiable: very large eigenvalue." The error suggests rescaling variables, which solves the problem. Here I've re-scaled by divided age in months by 12, to get age in years. **Let's make sure to talk about this.**

```{r}
r_all_par_rescaled <- lmer(score ~ target_num * factor
                           + (target_num + factor | ResponseId),
                           efa_all_par_scores %>%
                             mutate(target_num = target_num/12))
summary(r_all_par_rescaled, corr = F)
```

As we predicted (H2), we see dramatic increases in mental capacity attributions across the age range (main effect of `target_num`).

And also as we predicted (H1), we see differences across factors in where newborns are perceived to start off: Relative to the grand mean, newborns are perceived to start off with more "negative emotions" (distress, frustration, etc.; main effect of `factor1`), less/fewer capacities in the domain of "cognition and control" (emotional control, self control, etc.; main effect of `factor2`), and relatively more "bodily sensations" (pain, fatigue, etc.; main effect of `factor3`). (We could recode this to look at `factor4`, or just eyeball it from the plot.) Also as predicted, we see that the perceived changes across age vary dramatically across factors: "negative emotions" are perceived to change relatively less over development, "cognition and control" are perceived to change much more over development, and "bodily sensations" are predicted to chagne relatively less.

This is all very much in line with our preregistered hypotheses :)

Now let's see what the polynomial effects look like (again, looking at age in years instead of months). As we expected, including all of the polynomial effects as random slopes caused the model not to converge (I think we must be calculating df wrong), so I implemented our preregistered remedy and included only the linear effect as a random slope.

```{r}
# adding polynomial effects
r_all_par_poly <- lmer(score ~ poly(target_num, 3) * factor
                       + (poly(target_num, 1) + factor | ResponseId),
                       efa_all_par_scores %>%
                         mutate(target_num = target_num/12))
summary(r_all_par_poly, corr = F)
```

Lots to sift through here, but in general we see that the effect of target age on mental capacity attributions definitely has linear, quadratic, and cubic components, all three of which seem to vary substantially across factors. Pretty much all of these differences are "significant" (if you consider |t| > 2 to be "significant") - for interpretation, I would need to look closer at the plot. Let's pull it up again here, with blue lines approximating the formula `y ~ poly(x, 3)`:

```{r, fig.width = 4, fig.asp = 0.7}
scoresplot_fun(efa_all_par, target = "all (study 2)", 
               target_encoding = "numeric") +
  scale_x_continuous("age (months)", breaks = seq(0, 60, 12)) +
  geom_smooth(method = "lm", formula = "y ~ poly(x, 3)",
              color = "blue", size = 2)
```

We can talk through these interpretations together - but I find the difference between Factor 2 ("cognition & control") and Factor 4 ("positive emotions") to be especially interesting!


# Demographics

```{r, include = T}
ggplot(d_demo, aes(x = Duration/60)) + 
  geom_histogram(binwidth = 2) +
  geom_vline(xintercept = median(d_demo$Duration/60), color = "blue", lty = 2) +
  scale_x_continuous(breaks = seq(0, 10000, 4)) +
  labs(title = "Duration of study (according to Qualtrics)",
       subtitle = "Blue dotted line marks median",
       x = "Duration (in minutes)",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, Duration) %>%
  mutate(Duration = Duration/60) %>%
  summarise(median = median(Duration),
            mean = mean(Duration, na.rm = T),
            sd = sd(Duration, na.rm = T),
            min = min(Duration, na.rm = T),
            max = max(Duration, na.rm = T))
```

```{r, include = T}
ggplot(d_demo, aes(x = Age)) + 
  geom_histogram(binwidth = 2) +
  geom_vline(xintercept = median(d_demo$Age), color = "blue", lty = 2) +
  scale_x_continuous(breaks = seq(0, 10000, 4)) +
  labs(title = "Particpiant age (self-reported)",
       subtitle = "Blue dotted line marks median",
       x = "Age (in years)",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, Age) %>%
  summarise(median = median(Age),
            mean = mean(Age, na.rm = T),
            sd = sd(Age, na.rm = T),
            min = min(Age, na.rm = T),
            max = max(Age, na.rm = T))
```

```{r, include = T}
ggplot(d_demo, aes(x = GenderSex)) + 
  geom_bar() +
  labs(title = "Particpiant gender/sex (self-reported)",
       x = "Gender/sex",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, GenderSex) %>%
  count(GenderSex) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2))
```

```{r, include = T}
ggplot(d_demo, aes(x = gsub('(.{1,30})(\\s|$)', '\\1\n', RaceEthnicity_collapse))) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Particpiant race/ethnicity (self-reported)",
       x = "Race/ethnicity",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, RaceEthnicity_collapse) %>%
  count(RaceEthnicity_collapse) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2)) %>%
  arrange(desc(n))
```

```{r, include = T}
ggplot(d_demo, aes(x = FirstLang)) + 
  geom_bar() +
  labs(title = "Particpiant first language (self-reported)",
       x = "Language",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, FirstLang) %>%
  count(FirstLang) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2)) %>%
  arrange(desc(n))
```

```{r, include = T}
ggplot(d_demo, aes(x = factor(Education,
                              levels = levels(d$Education),
                              labels = gsub('(.{1,30})(\\s|$)', '\\1\n', 
                                            levels(d$Education))))) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Particpiant educational attainment (self-reported)",
       x = "Highest level of education completed",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, Education) %>%
  count(Education) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2))
```

```{r, include = T}
ggplot(d_demo, aes(x = Income)) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Particpiant household income (self-reported)",
       x = "Annual household income",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, Income) %>%
  count(Income) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2))
```

```{r, include = T}
ggplot(d_demo, aes(x = HouseholdSize)) + 
  geom_histogram(binwidth = 1) +
  geom_vline(xintercept = median(d_demo$HouseholdSize), color = "blue", lty = 2) +
  scale_x_continuous(breaks = seq(0, 10000, 1)) +
  labs(title = "Particpiant household size (self-reported)",
       subtitle = "Blue dotted line marks median",
       x = "Number of people in household (adults and children)",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, HouseholdSize) %>%
  summarise(median = median(HouseholdSize),
            mean = mean(HouseholdSize, na.rm = T),
            sd = sd(HouseholdSize, na.rm = T),
            min = min(HouseholdSize, na.rm = T),
            max = max(HouseholdSize, na.rm = T))
```

```{r, include = T}
ggplot(d_demo, aes(x = MaritalStatus)) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Particpiant marital status (self-reported)",
       x = "Marital status",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, MaritalStatus) %>%
  count(MaritalStatus) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2))
```

```{r, include = T}
ggplot(d_demo, aes(x = Parent)) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Particpiant parent status (self-reported)",
       subtitle = "'NA' indicates response of 'Prefer not to say'",
       x = "Parent status",
       y = "Number of participants")
```

```{r, include = T}
d_demo %>%
  distinct(ResponseId, Parent) %>%
  count(Parent) %>%
  mutate(prop = round(n/sum(n, na.rm = T), 2))
```

```{r, include = T}
ggplot(d_demo %>% filter(Parent == "Yes"), aes(x = ChildrenNumber)) + 
  geom_histogram(binwidth = 1) +
  geom_vline(xintercept = median(d_demo[d_demo$Parent == "Yes",]$ChildrenNumber, na.rm = T), 
             color = "blue", lty = 2) +
  scale_x_continuous(breaks = seq(0, 10000, 1)) +
  labs(title = "Number of children among parents (self-reported)",
       subtitle = "Blue dotted line marks median",
       x = "Number of children (among parents)",
       y = "Number of participants")
```

```{r, include = T}
ggplot(d_demo %>% filter(Parent == "Yes"), 
       aes(x = factor(ChildrenOldestAge_collapse,
                      levels = levels(d_demo$ChildrenOldestAge_collapse),
                      labels = gsub('(.{1,30})(\\s|$)', '\\1\n', 
                                    levels(d_demo$ChildrenOldestAge_collapse))))) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Age of oldest child among parents (self-reported)",
       x = "Age of child in years (among parents)",
       y = "Number of participants")
```

```{r, include = T}
ggplot(d_demo %>% filter(Parent == "Yes"), 
       aes(x = factor(ChildrenYoungestAge_collapse,
                      levels = levels(d_demo$ChildrenYoungestAge_collapse),
                      labels = gsub('(.{1,30})(\\s|$)', '\\1\n', 
                                    levels(d_demo$ChildrenYoungestAge_collapse))))) + 
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Age of youngest child among parents (self-reported)",
       x = "Age of child in years (among parents)",
       y = "Number of participants")
```



# Planning for S3 prereg

```{r}
d_temp <- d_all %>%
  rownames_to_column("subid") %>%
  mutate(target = gsub("^.*_target", "target", subid),
         ResponseId = gsub("_target.*$", "", subid),
         target_num = recode(target,
                             "target00mo" = 0,
                             "target0Xmo" = round(4/30, 3),
                             "target01mo" = 1,
                             "target02mo" = 2,
                             "target04mo" = 4,
                             "target06mo" = 6,
                             "target09mo" = 9,
                             "target12mo" = 12,
                             "target18mo" = 18,
                             "target24mo" = 24,
                             "target36mo" = 36,
                             "target48mo" = 48,
                             "target60mo" = 60),
         target_ord = recode_factor(target,
                                    "target00mo" = "newborns",
                                    "target0Xmo" = "4-day-olds",
                                    "target01mo" = "1-month-olds",
                                    "target02mo" = "2-month-olds",
                                    "target04mo" = "4-month-olds",
                                    "target06mo" = "6-month-olds",
                                    "target09mo" = "9-month-olds",
                                    "target12mo" = "12-month-olds",
                                    "target18mo" = "18-month-olds",
                                    "target24mo" = "2-year-olds",
                                    "target36mo" = "3-year-olds",
                                    "target48mo" = "4-year-olds",
                                    "target60mo" = "5-year-olds")) %>%
  select(-subid, -target) %>%
  gather(capacity, response, -starts_with("target"), -ResponseId)
```

```{r}
factors_temp <- efa_all_par$loadings[] %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  group_by(capacity) %>%
  top_n(1, loading) %>%
  ungroup() %>%
  arrange(factor, desc(loading)) %>%
  mutate(order = 1:nrow(.))

factors_temp
```

```{r}
capacities_s3 <- data.frame(capacity = c("controlling_their_emotions",
                                         "reasoning_about_things",
                                         "getting_hungry", 
                                         "feeling_pain",
                                         "feeling_happy", 
                                         "learning_from_other_people",
                                         "feeling_distressed", 
                                         "feeling_helpless"),
                            domain = c(rep("COG", 2),
                                       rep("BOD", 2),
                                       rep("POS", 2),
                                       rep("NEG", 2))) %>%
  mutate(domain = factor(domain))
```

```{r}
d_temp_culled <- d_temp %>%
  full_join(capacities_s3) %>%
  full_join(factors_temp) %>%
  filter(!is.na(domain)) %>%
  distinct()

# d_temp_culled
```


```{r}
contrasts(d_temp_culled$domain) <- contr.sum(reten_all_par)

r_temp <- lmer(response ~ target_num * domain
               + (target_num + domain | ResponseId) 
               + (target_num | capacity),
               d_temp_culled %>%
                 mutate(target_num = target_num/12))
summary(r_temp, corr = F)
```

```{r}
contrasts(d_temp_culled$domain) <- contr.sum(reten_all_par)

r2_temp <- lmer(response ~ target_num * domain
                + (target_num + domain | ResponseId)
                + (1 | capacity),
                d_temp_culled %>%
                  mutate(target_num = target_num/12))
summary(r2_temp, corr = F)
```

```{r}
contrasts(d_temp_culled$domain) <- contr.sum(reten_all_par)

r3_temp <- lmer(score ~ target_num * domain
                + (target_num | ResponseId),
                d_temp_culled %>%
                  mutate(target_num = target_num/12) %>%
                  group_by(domain, target_num, ResponseId) %>%
                  summarise(score = mean(response, na.rm = T)) %>%
                  ungroup() %>%
                  distinct())
summary(r3_temp, corr = F)
```

```{r}
contrasts(d_temp_culled$domain) <- contr.sum(reten_all_par)

r4_temp <- lmer(response ~ target_num
                + (1 | ResponseId)
                + (1 + target_num | capacity),
                d_temp_culled %>%
                  mutate(target_num = target_num/12) %>%
                  filter(domain == "NEG"))
summary(r4_temp, corr = F)

r5_temp <- lmer(response ~ target_num
                + (1 | ResponseId)
                + (1 | capacity),
                d_temp_culled %>%
                  mutate(target_num = target_num/12) %>%
                  filter(domain == "NEG"))
summary(r5_temp, corr = F)
```